Big data analysis techniques for intelligent systems

Farouk, Ahmed; Dou, Zhen

doi:10.3233/jifs-179109

Cited by 20 publications

(16 citation statements)

References 50 publications

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“…In the probabilistic model of text representation, it is assumed that there is a sufficiently large ideal text corpus document set and a string used by the user for query so that the query processing can be regarded as processing the attributes of the ideal result document set. Some attributes of the documents cannot be obtained exactly, because some feature attributes are fuzzy and invisible at the time of retrieval and contribute differently to the importance of the task [ 12 ]. Therefore, the probability values of these attributes need to be estimated at the early stage of the query to determine the importance of the processing task.…”

Section: Analytical Design Of English Writing Neural Network Algorithmmentioning

confidence: 99%

Construction of an Artificial Intelligence Writing Model for English Based on Fusion Neural Network Model

Hsiao

Hung

2022

Computational Intelligence and Neuroscience

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This paper presents an in-depth study and analysis of the model of English writing using artificial intelligence algorithms of neural networks. Based on word vectors, the unsupervised disambiguation, and clustering of multimedia contexts extracted from massive online videos, the disambiguation accuracy reaches over 0.7, and the resulting small-scale multimedia context set can cover up to 90% of vocabulary learning tasks; user experiments show that the multimedia context learning system based on this method can improve the effectiveness and experience of ESL vocabulary learning, as well as the long-term word sense memory of learners. The results are 30% better. Based on the dependency grammatical relations and semantic metrics of collocations on a large-scale professional corpus, we established a collocation intention description and retrieval method in line with users’ linguistic cognition and doubled the usage rate of collocation retrieval on the actual deployment system after half a year, becoming a user “sticky” ESL writing aid, and further defined style. Dictionaries only provide basic lexical definitions, and, even if supported by example sentences, they still cannot meet the needs of ESL authors in terms of expressive accuracy and richness. However, the current machine translation is based on the black box deep neural network construction, and its translation process is not understandable and interactive. Among the three algorithmic models constructed in this paper, the multitask learning model outperforms the conditional random field model and the LSTM-CRF model because the multitask learning model with auxiliary tasks solves the problem of sparse data to a certain extent, allowing the model to be trained more adequately in the case of uneven label distribution, and thus performs better than other models in the task of grammatical error detection.

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Section: Analytical Design Of English Writing Neural Network Algorithmmentioning

confidence: 99%

Construction of an Artificial Intelligence Writing Model for English Based on Fusion Neural Network Model

Hsiao

Hung

2022

Computational Intelligence and Neuroscience

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“…Some researchers start from the basis of learning theory and verify through experiments that learners' learning effect is significantly improved with the assistance of new technology, which lays a solid foundation for further research and application of mobile APP learning [8]. Farouk A et al conducted experiments on discrete data modeling and simulation of intelligent interactive English teaching, and the experiments focused on whether the service is effective when applied to educational teaching and learning [9]. Bryndin E et al conducted a study on smart interactive English teaching discrete data modeling and simulation, and the results indicated that discrete data modeling is perfectly feasible as a mobile APP learning aid for educational teaching.…”

Section: Related Workmentioning

confidence: 99%

Intelligent Interactive English Teaching Discrete Data Modeling and Simulation

Xin

2022

Scientific Programming

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This study attempts to explore the discrete data modeling and simulation of smart interactive English teaching based on mobile APP, expecting to explore the effective teaching strategies for deep integration of mobile APP technology and smart interactive English teaching, and to conduct discrete data modeling and simulation of English teaching. In this study, we propose a discrete data modeling algorithm for smart interactive English teaching based on discrete data points and normal vectors using B splines. By adding normal vector constraints, this method can maintain good fitting results even when the data points are discrete. The method consists of four parts, namely, discrete point parameterization, B-spline dominant point selection, node vector determination, and least-squares fitting, including both data point error and normal vector error. The mobile APP-based initial intelligent interactive English teaching should adhere to the four principles of autonomy of English course learning, adhere to teacher dominance in the language classroom, grasp the humanistic nature of English course content, and focus on the practical nature of English course implementation. The interactive learning environment based on mobile APP can promote students’ in-depth understanding of knowledge. The higher the student’s interest in learning, the deeper the degree of understanding; the more diverse the learning methods, the more it can promote students’ in-depth understanding of knowledge; the stronger the degree of interaction, the higher the level of understanding. The discrete data modeling of smart interactive English teaching proposed in this study provides a theoretical reference for the design and production of interactive learning resources for English courses, according to which the interactive learning resources for English courses are produced to make up for the lack of English course resources.

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“…Ghorbani and Ghousi conducted an in-depth study on educational management and data mining techniques in colleges and universities, using data mining related algorithms, and integrated a simple voting strategy through the study of simple improvement method of Apriori algorithm and ID3 algorithm, which has improved the educational management of students in colleges and universities and provided valuable guidance for students' training [ 10 ]. Farouk and Zhen used the educational technology of OLAM to analyze the education and admission of colleges and universities and identified the reasons and potentially useful information affecting students' enrolment through data mining techniques based on the educational data of previous years [ 11 ].…”

Section: Related Workmentioning

confidence: 99%

“…Through the above literature, we can see that data mining is more oriented to the improvement of algorithms of data mining technology and the mining of existing education management data to analyze the intrinsic relationship between their different attribute values, but there is relatively little research on the valuable information that affects the relationship between new students' arrival rate and education management decisions. The purpose of this paper is to analyze and study educational management data from this perspective using mining techniques [ 11 , 18 ]. The development of an educational management information management system plays an important role in promoting the development of higher education informatization.…”

Section: Related Workmentioning

confidence: 99%

Model Construction and Research on Decision Support System for Education Management Based on Data Mining

Wang

2021

Computational Intelligence and Neuroscience

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Based on data mining technology, this paper applies a combination of theoretical and practical approaches to systematically describe the background and basic concepts related to the generation of data mining-related technologies. The classical data mining process is analyzed in depth and in detail, and the method of building a decision support system for education management based on the B/S model is studied. Not only are the data mining techniques applied to this system, but also the decision tree model with the improved ID3 algorithm is implemented in this thesis, which is further applied to the educational management decision support system of this topic. The load of the client computer is reduced, and the client computer only needs to run a small part of the program. This paper focuses on the following aspects: the overall planning of the educational management decision support system based on data mining technology. From the actual educational management work, we analyze the requirements and design each functional module of this system in detail, applying the system functional structure diagram and functional use case diagram to represent the functional structure of the system and using flow charts to illustrate the workflow of the system as a whole and in parts. The logical structure design, entity-relationship design, and physical model design of the database have been carried out. To improve the efficiency of the system, the ID3 algorithm was improved on this basis to reduce the time complexity of its operation, improve the efficiency of the system operation, and achieve the goal of assessing and predicting the teaching quality of teachers. The development and design of this system provide an efficient, convenient, scientific, and reliable system tool to reduce the workload of education administrators and, more importantly, to make reasonable and effective use of the large amount of data generated in the management, and data mining techniques are used to extract valuable and potential information from these data, which can be more scientific and efficient for the teaching of teachers and students. It can provide reliable, referenceable, and valuable information for managers to make assessments and decisions.

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Big data analysis techniques for intelligent systems

Cited by 20 publications

References 50 publications

Construction of an Artificial Intelligence Writing Model for English Based on Fusion Neural Network Model

Construction of an Artificial Intelligence Writing Model for English Based on Fusion Neural Network Model

Intelligent Interactive English Teaching Discrete Data Modeling and Simulation

Model Construction and Research on Decision Support System for Education Management Based on Data Mining

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